批判性审视机器人智能的‘具身性’，指出当前AI赋能机器人仍很脆弱。
Embodied AI in Machine Learning -- is it Really Embodied?
- 将现代AI机器人与经典认知主义和具身智能对比，揭示其本质缺陷。
- 指出当前机器人虽用大模型但具身能力弱，仍存传统AI问题。
- 分析跨具身学习障碍，提出改进方向，适合关注机器人哲学的读者。
具身人工智能（Embodied AI）在机器学习领域日益流行，旨在利用深度学习、变换器及大语言/视觉-语言模型等进展来增强机器人能力。本文将其置于‘老式人工智能’（GOFAI）与基于行为或具身替代方案的语境中进行探讨。我们主张，当前由AI驱动的机器人仅具有弱具身性，且继承了部分GOFAI的问题。此外，本文回顾并批判性讨论了跨具身学习的可能性（Padalkar et al. 2024），识别出根本性障碍，并提出推动进步的方向。
原文摘要 · Abstract (English)
Embodied Artificial Intelligence (Embodied AI) is gaining momentum in the machine learning communities with the goal of leveraging current progress in AI (deep learning, transformers, large language and visual-language models) to empower robots. In this chapter we put this work in the context of "Good Old-Fashioned Artificial Intelligence" (GOFAI) (Haugeland, 1989) and the behavior-based or embodied alternatives (R. A. Brooks 1991; Pfeifer and Scheier 2001). We claim that the AI-powered robots are only weakly embodied and inherit some of the problems of GOFAI. Moreover, we review and critically discuss the possibility of cross-embodiment learning (Padalkar et al. 2024). We identify fundamental roadblocks and propose directions on how to make progress.
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